27. June 2022

What to expect from this course

  • Introduce some basic data science skills
  • Demonstrate how to work effectively in cross-disciplinary teams
  • Give an opportunity to learn and produce something worklife related

Who are we?

Solveig Bjørkholt (PhD in political science)

Sondre Elstad (PhD in economics)

Alex Moltzau (AI Policy and Ethics at NORA)

Who are you? :)

  • What’s your name?
  • What do you study?
  • Have you used any R before?
  • What would you like to get out of this course?

What is a hackathon?

  • Hacking: Problem solving
  • An event where people come together for a set duration of time to solve problems.

Kind of like a festival!


What is “political data science”?

Political science: Domain knowledge about power relations, institutions and broad analytical skills for studying social phenomena.

Course material

  • Main course material
  • R for Data Science
  • Storytelling with data
  • Text mining with R
  • Machine Learning for Social Scientists

But consider these learning resources rather than syllabus. The goal here is to learn through creating, so use these resources to look up things you wonder about rather than read them from A to Z.

Course overview

  • Week 1: Team work, Github, R, RStudio and R Markdown. Presentations from stakeholders.

  • Week 2: Data gathering and visualization.

  • Week 3: Text analysis.

  • Week 4: Machine learning.

  • Week 5: Machine learning, iterative work, deployment and IT communication.

  • Week 6: Finishing projects and final presentations.

The “final exam”

  1. An R Markdown report exploring the case (optionally including a dashboard)
  2. A Github repository with reproducible code
  3. A team presentation at the 4th of August for the stakeholders and others


  • The course ends with a pass/fail grade.
  • And a reward for the winner team.

The one-up awards

  • We are a very diverse group in terms of backgrounds, programming competencies and so forth
  • To capitalize on that, we need to help each other out

The one-up awards: Two awards to the two students who make a particular effort to help others out.

Location location




Monday 27. June to Wednesday 13. July (week 1, 2 and 3)




Monday 18. July to Thursday 4. August (week 4, 5, and 6)

Cases and stakeholders

Case descriptions can be found in Canvas.

  • SSB: How can we contribute to better official statistics using webscraping to create an indicator of statistical quality from the “About the statistics” part on www.ssb.no?

  • NORAD: How can we use classification algorithms to discover whether an aid agreement also contributes to reduction of greenhouse gas emissions?

  • UNA (FN-sambandet): How can we use the storingscrape API to explore the political effect of UNA?

  • OAG (Riksrevisjonen): How can we use various data sources on health statistics to map the efficiency and quality of the Norwegian health care system?

  • OPX: How can we use various data sources on NGOs to create a product that maps the most efficient and robust NGOs for investors?

Group 1: Statistics Norway

Group 1 - SSB
1 Abdullah Mohamed Reda Almudaffar Faculty of Mathematics and Natural Sciences Robotics (Informatics) Bachelor
2 Beate Solstrand Baklund Faculty of Social Sciences Political Science Bachelor
3 Tyra Larsdatter Grasmo Faculty of Social Sciences Sociology Bachelor
4 Vera Erikstad Rutherfurd Faculty of Mathematics and Natural Sciences Mathematics with Informatics Bachelor
5 Nora Christina Lokken Faculty of Social Sciences Master of Economics (2-year) Master
Case SSB: Webscraping, structuring, automated program

Group 2: NORAD

Group 2 - NORAD
1 Eira Henden Nybakk Faculty of Social Sciences Political Science Bachelor
2 Nora Didriksen Faculty of Social Sciences Political Science Bachelor
3 Nikolai Elias Koop Faculty of Mathematics and Natural Sciences Informatics: Digital Economics and leadership Bachelor
4 Oda Strand Marchand Faculty of Humanities European languages - Russian language Bachelor
5 Kasim Sadikovic Faculty of Mathematics and Natural Sciences Mathematics Bachelor
Case Norad: Data collection, text analysis, classification

Group 3: FN-sambandet

The United Nations Association of Norway

Group 3 - FN-sambandet
1 Markus Annæus Austreim Opheim Faculty of Social Sciences Samfunnsøkonomi (bachelor) Bachelor
2 Hannes Bräuer Faculty of Social Sciences Peace and Conflict Studies Master
3 Andreas Lind Kroknes Faculty of Social Sciences Master program Political Science/Statsvitenskap - Master Master
4 Gard Olav Dietrichson Faculty of Social Sciences Political Science Master
5 Jørn Lager Lyngås Faculty of Social Sciences Sosialantropologi Bachelor
Case FN-sambandet: API, data structuring

Group 4: Riksrevisjonen

The Office of the Auditor General of Norway

Group 4 - Riksrevisjonen
1 Xhensila Kllogjeri Faculty of Social Sciences Samfunnsøkonomi Bachelor
2 Christine Diane Malaca Morte Faculty of Law Master i rettsvitenskap Master
3 Izolda Vlasova Faculty of Social Sciences Samfunnsøkonomi Bachelor
4 Anton Kristian Bugge Faculty of Social Sciences Political Science Bachelor
5 Hedvig Signy Monsen Kristoffersen Faculty of Social Sciences Master in Economic Theory and Econometrics Master
6 Willem Ofstad Faculty of Social Sciences Master of Economic Theory and Econometrics Master
Case Riksrevisjonen: Data collection, exploratory analysis

Group 5: Oslo Philanthropic Exchange (OPX)

Group 5 - OPX
1 Agnieszka Sadlowska Faculty of Social Sciences Statsvitenskap Bachelor
2 Torbjørn Skinnemoen Ottersen Faculty of Social Sciences Public administration and leadership Bachelor
3 Ida Kristine Garthe Faculty of Humanities Chinese Culture and Society Master
4 Henrik Waage Rui Faculty of Social Sciences Political Science Bachelor
5 Amalie Halle Christensen Faculty of Social Sciences Masters in Economic Theory and Econometrics Master
Case OPX: Data collection and indicator building

The stakeholder presentations

On Friday the 1st of July

Organization Presenter Time Group
Statistics Norway (SSB) Xeni Kristine Dimakos 10:15 - 10:35 1
United Nations Association of Norway (FN-sambandet) Nicholas Wilkinson 10:40 - 11:00 3
Norad Einar Tornes 11:05 - 11:25 2
Office of the Auditor General of Norway (Riksrevisjonen) Aleksander Eilertsen 11:30 - 11:50 4
OPX (Oslo Philanthropic Exchange) Bosse Langaas 11:55 - 12:15 5

Agile working methods

The agile manifesto:

  • Individuals and interactions over processes and tools
  • Working software over comprehensive documentation
  • Customer collaboration over contract negotiation
  • Responding to change over following a plan

Agile working methods

  • Minimum viable product (MVP): The least finished product you can make that will still allow you to test the functionality.

  • Sprints: Short sessions of work (e.g. one week) with clearly defined tasks

  • Stakeholders: Frequently talk to the people who are going to use your product – does it address their needs?

  • Roles:

    • Product owner: Knows what the product should look like in the end
    • Team: Developers of the product
    • Scrum master: Facilitates the work

Agile working methods

The Belbin Strength Test

How to capitalize on each others’ strengths?

The Belbin Strength Test

Find the test in Canvas.

Questions?